Artificial Neural Network Modeling of Unsteady Aerodynamic Characteristics of Aircraft at High Attack Angle

The simulation and modeling of aerodynamic characteristics at high angle of attack is one of the most important research areas for new concept aircraft. Based on simplified missile model, firstly, aerodynamic characteristics of 70å ttack angle were numerically simulated by RANS-based CFD method with SA turbulence model. The finite volume method was used to discretize the N-S formulation. The LU-SGS dual time-stepping algorithm was used for time marching. The unsteady calculations with five different oscillation frequencies were carried out in the mode of forced oscillation, and the final aerodynamic data in each iteration period were recorded. Secondly, based on CFD results, traditional methods such as dynamic derivative model and polynomial model were used for aerodynamic modeling, and the validity and accuracy were analyzed. Finally, dynamic derivative model, polynomial model and neural network mode were used to modeling aerodynamic characteristics. The results show that the artificial intelligence aerodynamic modeling method based on neural network has higher accuracy and adaptability. This method provides theoretical technical support for the unsteady nonlinear aerodynamic modeling, and the stability analysis and control of aircrafts at high angle of attack.